How to Make a Correlation Matrix for Some Variables and not all of Them-CodePudding

I have a dataset with 70 variables. The name of variables is like bio1 to bio70. I need to check the correlation of one variable, such as bio2, against the other 70 variables only. I used the following codes

## Generate scatterplot matrix
splom(MyData, panel = panel.smoothScatter, raster= TRUE, na=TRUE)
# Generate Correlations
cor(MyData, use = "pairwise.complete.obs")
corrplot.mixed(cor(MyData, use="pairwise.complete.obs"), lower.col = "black")

But these codes make a 70 by 70 matrix for me that I do not need it. How can I change these codes to give me the correlation matrix of one variable, such as bio2, against other variables? Thanks

CodePudding user response：

You didn't provide a dataset so I'll show you with R's iris dataset instead using both the tidyverse and correlation packages. First load the libraries:

#### Load Libraries ####
library(correlation)
library(tidyverse)

Then from there you can run a correlation matrix with the following code:

#### Correlation Matrix Default ####
iris %>% 
  correlation()

# Correlation Matrix (pearson-method)

Parameter1   |   Parameter2 |     r |         95% CI | t(148) |         p
-------------------------------------------------------------------------
Sepal.Length |  Sepal.Width | -0.12 | [-0.27,  0.04] |  -1.44 | 0.152    
Sepal.Length | Petal.Length |  0.87 | [ 0.83,  0.91] |  21.65 | < .001***
Sepal.Length |  Petal.Width |  0.82 | [ 0.76,  0.86] |  17.30 | < .001***
Sepal.Width  | Petal.Length | -0.43 | [-0.55, -0.29] |  -5.77 | < .001***
Sepal.Width  |  Petal.Width | -0.37 | [-0.50, -0.22] |  -4.79 | < .001***
Petal.Length |  Petal.Width |  0.96 | [ 0.95,  0.97] |  43.39 | < .001***

p-value adjustment method: Holm (1979)
Observations: 150

If you want to select only the sepal variables, you can use this code instead:

#### Only Use Sepal Variables ####
iris %>% 
  select(Sepal.Length,
         Sepal.Width) %>% 
  correlation()

Giving you this limited matrix now:

# Correlation Matrix (pearson-method)

Parameter1   |  Parameter2 |     r |        95% CI | t(148) |     p
-------------------------------------------------------------------
Sepal.Length | Sepal.Width | -0.12 | [-0.27, 0.04] |  -1.44 | 0.152

p-value adjustment method: Holm (1979)
Observations: 150

An alternative way of doing this is by deselecting the variables you dont want:

#### Alternative ####
iris %>% 
  select(-Petal.Length,
         -Petal.Width) %>% 
  correlation()

Edit

Seems you also wanted a correlation plot. I prefer using ggcorrplot cuz it looks better and its easier to work with. Here is a simple one only deselecting one variable from the matrix:

#### Using ggcorrplot ####
library(ggcorrplot)
corr <- iris %>% 
  select(-Petal.Length) %>% 
  correlation()
corr
ggcorrplot(corr = corr,
           type = "lower",
           lab = T)

Giving you this: